Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Abstract

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning

“…36 • 38 ′ 56 ′′ N; 51 • 29 ′ 20 ′′ E; 23 m a.s.l) characterized mean annual precipitation to be around 1300 mm. October and August are the wettest and driest months, respectively (Haghshenas et al, 2016;Moradi et al, 2022).…”

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

“…In [18], to estimate the AGB from remotely sensed data, parametric and non-parametric methods, including Multiple Regression (MR), k-Nearest Neighbour (kNN), Random Forest, the multi-layer perceptron, which performed best among the various methods, were applied to a single Sentinel-2 image using spectral bands and derived indices. Similarly, in [19] the authors explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modelling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site), showing that the RF and XGBoost models had a robust and efficient performance and that the introduction of eight grey-level co-occurrence matrix (GLCM) textures moderately improved the accuracy of modelling AGB.…”

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

“…The remote sensing method facilitates AGB monitoring without destroying any existing vegetation, while striking a good balance between temporal and spatial scales [23,24]. Its basic idea is to establish a correlation between remote sensing image features and sample point-based measured data, and then use parametric models or machine learning methods to calculate AGB [25][26][27]. Multiple linear regression is an important method for inferring vegetation AGB because it is highly interpretable and understandable [28,29].…”